1. APHLIS for improved Food Security Planning Postharvest Losses Information System APHLlS

2. APHLIS – the slideshow What is APHLIS and what problems does it address How you can get PHL estimates from the system How you can generate your own PHL estimates The way forward

3. APHLIS - a unique service APHLIS generates estimates of postharvest losses (PHLs) of cereals in East and Southern Africa and is Based on a network of local experts who submit data and verify loss estimates Built on a complete survey of the literature on PHLs APHLIS provides …… Loss estimates by cereal, by country and by province that are updated annually A display of the data used to derive losses so the system is fully transparent, and The opportunity to add better loss data so that loss estimation can improve over time

4. Postharvestchain What are Postharvest Losses (PHLs)? PHLs (of cereals) are the cumulative weight losses from production from each link in the postharvest chain (including all grain not fit for human consumption but not PHLs from processing e.g. milling). Maize % weight losses 2007 from provinces of Zimbabwe and Ethiopia

5. The Problem APHLlS Soaring food prices and the economic recession are hampering efforts to reduce poverty. PHLs have negative impacts on hunger, poverty alleviation, income generation and economic growth. Yet the magnitude and location of such losses are poorly understood because PHL figures are mostly guesstimates relatively difficult to trace for both logic and info source, and the sources themselves may not be very reliable

6. By improving PHL estimates it will be possible in the short term to - Improve food security arrangements by calculating food supply estimates more reliably from production figures ….and long-term to target loss reduction interventions at – the most affected areas (geographically) the most affected links in the postharvest chain or those that would be most cost effective to address, and The advantages of better PHL estimates APHLlS

7. A system for getting better PhL estimates The main elements of APHLIS are – Local expert network providing data and verifying PHLs Database with access to local experts, by country, PHL Calculator (model) that estimates losses Web site for display of loss data by cereal for each country and each province, in tables and in maps Downloadable calculator for PHL estimation at any geographical scale

8. A gric. data GIS maps of PHLs etc Data tables PHLs by crop country and province Network of local experts PHL database PHL calculator PHL tables Calculator spreadsheet APHLIS – the System in a nutshell Download

9. APHLIS network of experts – its most important resources to supply data and verify PHL estimates Network of local experts

10. How the PHL calculator works The PHL calculator determines a cumulative weight loss from production using loss figures for each link in the postharvest chain. A set of losses figures for the links of the postharvest chain is called a PHL profile Harvesting/field drying6.4 Drying4.0 Shelling/threshing1.2 Winnowing- Transport to store2.3 Storage5.3 Transport to market1.0 Market storage4.0 Example of a PHL profile for maize grain Figures taken from the literature or contributed by network experts

11. PHL Calculator contd PHL profiles are specific for Climate type (A – tropical, B - arid/desert, C – warm temperate) Crop type (different cereals) Scale of farming (subsistence/commercial) Climate typeACBBA Crop typeMaize SorghumMilletRice Scale of farmingSmallLargeSmall Harvesting/field drying6.42.04.93.54.3 Drying4.03.5--- Shelling/threshing1.22.34.02.52.6 Winnowing----2.5 Transport to store2.31.92.12.51.3 Storage5.32.12.21.11.2 Transport to market1.0 Market storage4.0 Five examples of PHL profiles

12. PHL Calculator contd The PHL profile values are modified according to – 1.Wet/damp weather at harvest 2.Length of storage period (0-3, 4-6, >6 months) 3.Larger grain borer infestation (for maize only) … and the PHL calculation takes into account – 4.The number of harvests annually (1, 2 or 3) 5.Amount of crop marketed or retained in farm storage NB PHL values are affected much more by the application of modifiers than by the initial selection of the PHL profile.

13. How to get a PHL estimate Two ways to get PHL estimates Consult the tables and/or maps on the website for losses by region, country or province Postharvest Losses Information System Losses estimates Losses maps (interactive) Literature Downloads PHL Network About us Contacts Links Production Yield Larger grain borer Average farm size Home

14. Loss tables APHLlS Regional losses for all cereals and by cereal type Click Estimated Postharvest Losses (%) 2003 - 2009

15. Loss tables by cereal type and country Click Estimated Postharvest Losses (%) 2003 - 2009

16. Loss tables by cereal type and province Click on one of these figures to get details of the loss calculation Estimated Postharvest Losses (%) 2003 - 2009

17. Details of the loss calculation. 1. Production data by farm type and losses over seasons Calculation matrix documenting the PH loss calculation quality of data sources and references to sources Country: Malawi Province: Area under National Administration Climate: Humid subtropical (Cwa) Year: 2007 Crop: Maize Production Annual production and losses Grain remaining Lost grain tonne Seasonal production and losses % SeasonFarm typeProduction (t) Remaining (%) Losses (t)Production (%)Remaining (t) Losses (%)

18. Details of the loss calculation 2. Factors modifying the PHL profile Rain at harvest – increases loss at harvest time. Larger Grain Borer – LGB attack doubles farm storage losses. Marketed at harvest % - divides the harvest between what is stored on farm and what is sent to market. Storage duration - loss increases with longer storage periods. Marketed at harvest (%) Rain at harvest Storage duration (months) Larger grain borer no data yes no data 20 PHL (%) calculation PHL (%) Calculation: Season: 1 Farm Type: small

19. Details of the loss calculation 3. The PHL profile and loss increments Stages Harvesting/field drying Platform drying Threshing and shelling Winnowing Transport to farm Farm storage Transport to market Market storage PH profile (adjusted) Remaining grainLoss increment Total 58.615.7 69.54.86.4 66.82.84 660.81.2 660- 64.41.52.3 58.65.89 58.601 04

20. Datum not a measured estimate Data overall specific to maize Details of the loss calculation 4. Quality of the data in the PH profile and references to data sources 1 0 Datum not specific to maize 0 Data overall not measured 0 The reference to Boxall 1998 StagesLoss figureReference CerealClimateFarm typeMethod References and individual loss figures % for small farms Origin of figure 6.4 5.0 9.5 5.8 9.9 2.0 Harvesting/field drying

21. The PHLs are also displayed on maps The PHLs are also displayed on maps APHLlS PHL values in 2007 Maize Sorghum Wheat

22. There are also maps of LGB by year APHLlS Locations where Larger Grain Borer (Prostephanus truncatus) was considered to be a significant pest in 2007

23. Getting your own PHL estimate - using the downloadable calculator The downloadable calculator lets you enter your own figures. It can Work at whatever geographical scale is relevant See all the details of the calculation Assess the reliability and see the origin of data Record multiple estimates and obtain weighted average PHLs

24. The downloadable calculator – front page You can change the default figures (in blue) Change language Open calculator

25. …………..changing the defaults You can change any of the default figures (in blue)

26. ……… observing the calculation Cumulative annual loss for one season PHL profiles for large-scale & small -scale maize farming in Cwa climate

27. Conclusions APHLIS generates PHL estimates for cereal grains that are - Transparent in the way they are calculated Contributed (in part) and verified by local experts Updated annually with the latest production figures Based on the primary national unit (i.e. province) Upgradeable as more (reliable) loss data become available

28. For the future For the future APHLIS …….. Would benefit from an effort to generate more PHL data. Should be made sustainable by efforts of the international community. Could be expanded in geographical range (W. Africa, Asia, S. America) and technical content (e.g. pulses) May be used in new ways, for example as unseasonal rain becomes more common the impact of this on PHLs can be predicted